7,752 research outputs found
The Radon Monitoring System in Daya Bay Reactor Neutrino Experiment
We developed a highly sensitive, reliable and portable automatic system
(H) to monitor the radon concentration of the underground experimental
halls of the Daya Bay Reactor Neutrino Experiment. H is able to measure
radon concentration with a statistical error less than 10\% in a 1-hour
measurement of dehumidified air (R.H. 5\% at 25C) with radon
concentration as low as 50 Bq/m. This is achieved by using a large radon
progeny collection chamber, semiconductor -particle detector with high
energy resolution, improved electronics and software. The integrated radon
monitoring system is highly customizable to operate in different run modes at
scheduled times and can be controlled remotely to sample radon in ambient air
or in water from the water pools where the antineutrino detectors are being
housed. The radon monitoring system has been running in the three experimental
halls of the Daya Bay Reactor Neutrino Experiment since November 2013
Prognostic value of troponins in acute coronary syndrome depends upon patient age
Peer reviewedPostprin
First-Order Transition and Critical End-Point in Vortex Liquids in Layered Superconductors
We calculate various thermodynamic quantities of vortex liquids in a layered
superconductor by using the nonperturbative parquet approximation method, which
was previously used to study the effect of thermal fluctuations in
two-dimensional vortex systems. We find there is a first-order transition
between two vortex liquid phases which differ in the magnitude of their
correlation lengths. As the coupling between the layers increases,the
first-order transition line ends at a critical point. We discuss the possible
relation between this critical end-point and the disappearance of the
first-order transition which is observed in experiments on high temperature
superconductors at low magnetic fields.Comment: 9 pages, 5 figure
Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters
Modern service robots will soon become an essential part of modern society. As they have to move and act in human environments, it is essential for them to be provided with a fast and reliable tracking system that localizes people in the neighbourhood. It is therefore important to select the most appropriate filter to estimate the position of these persons.
This paper presents three efficient implementations of multisensor-human tracking based on different Bayesian estimators: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Sampling Importance Resampling (SIR) particle filter. The system implemented on a mobile robot is explained, introducing the methods used to detect and estimate the position of multiple people. Then, the solutions based on the three filters are discussed in detail. Several real experiments are conducted to evaluate their performance, which is compared in terms of accuracy, robustness and execution time of the estimation. The results show that a solution based on the UKF can perform as good as particle filters and can be often a better choice when computational efficiency is a key issue
- …